Entropy regularization (ERL) is a method proposed by [1]} to alleviate overconfident predictions. Many existing approaches also exist which have been proven to materialize into ERL such as label smoothing [2]} and focal loss [3]}. We focus our attention to the ERL objective function and decipher the mode of working for this particular algorithm. The overall ERL training objective is
\(\mathcal {L} = \mathcal {L}_{ce} - \beta \mathcal {L}_{erl},\) 
